Not artificial intelligence. The layer between a robot's brain and its body that makes movement instant. Powered by the same Trinity Cortex succession engine that drives Validiti's language work, adapted for motor control. 0.2 ms decisions on a $5 microcontroller.
Per decision in microseconds, with the proof — every motor action on the record.
Four steps. VSS watches; patterns emerge; the prediction is instant.
Think of it like your hands on a keyboard. You don't calculate where each key is. Your fingers know. That's what MCI gives to machines.
Traditional robotic control stacks reach for the heaviest tool. MCI lives at the layer underneath — where speed, control-rate breadth, footprint, and what VSS remembers about each device decide whether a robot moves at all.
| Property | Policy NN (GPU) | Classical MPC | Validiti Reflex |
|---|---|---|---|
| Decision latency | 5–10 ms | 1–3 ms | 0.2 ms |
| Decision-rate breadth | 50–200 Hz typical | 100–1,000 Hz | 100 Hz – 5 kHz on the same engine |
| Hardware floor | $1,000+ Jetson / GPU | Industrial PC | $5 microcontroller |
| Power draw | 30–300 W | 15–60 W | ~5 mW (battery weeks) |
| Adaptation | Retrain (weeks) | Hand-tune (engineer hours) | Continuous observation |
| Pattern depth | Short context window (5–30 steps) | None — model-based, no memory | Multi-depth succession tables (2–10+ steps) |
| Data retention | Sample & summarize during training | Discarded each control cycle | Every trajectory, indexed per device |
| Per-machine quirks | Same model everywhere | Generic plant model | Each device learns its own |
| Offline operation | Cloud retraining loop | Yes | Yes — on-device entirely |
Three control loops, same wall-clock, running at their real rates. The numbers reset every three seconds — watch what each system kept while it was running.
In the same three-second window, MCI captures 15,000 trajectory snapshots while a policy NN gets to 600 — and only MCI keeps all of them.
Per-actuator muscle memory, on-device, microsecond-class. Markets that have been waiting for an inference layer that lives below the inference layer.
The same succession engine that drives Validiti's language work — adapted from words to joint states.
Pick the control rate your hardware needs. Pay per DOF at that tier's published rate. Apex caps any single device at $2,499/month. Fleet handles anything past 1,000 decisions/sec/DOF or large multi-robot deployments.
Hobby builds, student prototypes, reactive control loops. Single servos at slow rate, learning rigs, classroom kits.
Drone autopilot, pick-and-place arms, basic navigation, indoor robots, AGV path correction.
Quadruped terrain navigation, humanoid manipulation, assembly line AI, fast industrial arms.
Surgical precision, high-speed industrial, elite autonomous systems. The single-device cap means complex multi-axis machines don't scale punitively.
OEM embedding, defense, medical, full autonomous humanoids, multi-robot systems at scale.
Contact for fleet pricing →A DOF is one independently-controlled axis — a 7-joint arm is 7 DOF; a quadcopter's four motors plus four attitude states is 8 DOF. Tier brackets are the supported decision rate per DOF. Apex's $2,499/device monthly cap means a 50-DOF humanoid at the top of the Apex band pays the same as a 60-DOF one. Anything above 1,000 dec/sec/DOF, or fleet-scale multi-robot deployments, is Fleet: contact@validiti.com for negotiated annual.
Every Validiti SKU inherits the same Safe · Fast · Smart guarantees from the shared language — encryption, tamper-evident history, runtime defense, predictable performance. Same code, same proof, same floor on every install.
